Chenglong Li

In low earth orbit navigation augmentation (LEO-NA) systems, the power inversion (PI) adaptive arrays tend to misidentify LEO-NA signals as interference and thus suppress them. However, the impact of this suppression phenomenon has not been sufficiently investigated. This letter presents an analysis of the suppression mechanism affecting LEO-NA signals in PI adaptive arrays and proposes a theoretical model to quantify its impact. Based on the proposed model and the maximum signal-to-interference-plus-noise ratio (MSINR) criterion, the optimal power augmentation for LEO-NA signals can be determined in a fixed scenario. Both theoretical analysis and experimental results demonstrate that the optimal power augmentation level is approximately 23.9 dB for a LEO-NA signal operating at the B3I frequency in a single-satellite augmentation configuration.

Power enhancement is a primary focus in the Global Navigation Satellite System (GNSS) to improve its anti-interference capabilities. However, interference issues cannot be fully resolved due to the limited increase in signal power. Additionally, traditional interference mitigation methods often misclassify signals as interference and suppress them, leading to significant GNSS signal loss. This paper proposes an adaptive sparse filtering (ASF) method based on sensed equivalent bandwidth, enabling interference mitigation for GNSS power enhancement without additional signal loss. By analyzing the spectral relationship between interference and signal bandwidth, the equivalent bandwidth that ignoring the strong signal recognition is derived, and the sparse sampling model is developed to reconstruct the interference mitigation filter. Simulations and practical tests verify the effectiveness of this interference mitigation method, which is broadly applicable to both conventional and power-enhanced GNSS signals. It improves anti-interference performance by 3-5 dB without introducing additional computational complexity associated with spectral awareness. This improvement in signal quality also results in the 19% increase in positioning accuracy.

The Direction of Arrival (DOA) parameter is a key parameter in directional channel modeling for GNSS systems and multipath suppression. However, achieving high-precision, low-complexity DOA estimation of multiple signal sources without requiring a known source number is still a challenge. This paper introduces a satellite navigation DOA parameter estimation method based on deconvolution beamforming. By exploiting the translational invariance property of the uniform linear array pattern, the deconvolution process is applied to the de-spread array pattern of satellite navigation signals, achieving high-precision estimation of DOA parameters. This method can achieve high-precision blind DOA estimation of multiple signal sources while significantly reducing the estimation complexity. Compared with traditional methods, precise DOA estimation can be achieved even in low-signal-to-noise-ratio conditions and with a small number of elements in the array. The theoretical analysis and simulation results verify the effectiveness of the proposed algorithm.

In this paper, we present a novel localization scheme, location-aware ranging correction (LARC), to correct ranging estimates from ultra wideband (UWB) signals. Existing solutions to calculate ranging corrections rely solely on channel information features (e.g., signal energy, maximum amplitude, estimated range). We propose to incorporate a preliminary location estimate into a localization chain, such that location-based features can be calculated as inputs to a range-error prediction model. This way, we can add information to range-only measurements without relying on additional hardware such as an inertial measurement unit (IMU). This improves performance and reduces overfitting behavior. We demonstrate our LARC method using an open-access measurement dataset with distances up to 20 m, using a simple regression model that can run purely on the CPU in real-time. The inclusion of the proposed features for range-error mitigation decreases the ranging error 90th percentile (P90) by 58% to 15 cm (compared to the uncorrected range error), for an unseen trajectory. The 2D localization P90 error is improved by 21% to 18 cm. We show the robustness of our approach by comparing results to a changed environment, where metallic objects have been moved around the room. In this modified environment, we obtain a 56% better P90 ranging performance of 16 cm. The 2D localization P90 error improves as much as for the unchanged environment, by 17% to 18 cm, showing the robustness of our method. This method evolved from the first-ranking solution of the 2021 and 2022 International Conference on Indoor Position and Indoor Navigation (IPIN) Competition.

Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site - which were impossible to organise during 2021 and 2022 - in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.

Wireless-based human activity recognition has become an essential technology that enables contact-free human–machine and human–environment interactions. In this article, we consider contact-free multitarget tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6-GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then, the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets’ locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multipedestrian tracking experiment is conducted in a room with a size of 6.5 $\text{m}\times 10$ m to evaluate the performance of the proposed algorithm. According to the experimental results, we achieve 75th and 95th percentile accuracy of 12.7 and 18.2 cm for single-person tracking and 28.9 and 45.7 cm for multiperson tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real time, which is promising for practical MTT use cases.

Wireless-based human activity recognition has become an essential technology that enables contact-free human-machine and human-environment interactions. In this paper, we consider contact-free multi-target tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6 GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets' locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density filter for tracking. A multi-pedestrian tracking experiment is conducted in a room with size of 6.5 m$\times$10 m to evaluate the performance of the proposed algorithm. According to experimental results, we achieve 75th and 95th percentile accuracy of 12.7 cm and 18.2 cm for single-person tracking and 28.9 cm and 45.7 cm for multi-person tracking, respectively. Furthermore, the proposed algorithm achieves the tracking purposes in real-time, which is promising for practical MTT use cases.

Ultrawideband (UWB) attracts extensive attention for the Internet-of-Things applications, especially the fine-grained location-based services. Rather than active tracking, this article explores the UWB-based device-free pedestrian tracking problem. Concerning the challenges of fine-grained passive tracking for the low-cost commodity UWB devices, we propose a variance-based temporary-spatial (VATS) mapping algorithm, which relieves the background interference from the perspective of the Bayesian framework. Moreover, a particle filter algorithm has been designed to track the position likelihood changing and to avoid likelihood ambiguity. Experimental results show that the proposed VATS mapping algorithm achieves 50th and 90th percentile errors 0.156 and 0.272 m, respectively, which is promising for practical applications.

Due to its high delay resolution, the ultrawideband (UWB) technique has been widely adopted for fine-grained indoor localization. Instead of active positioning, UWB radar-based passive human tracking is explored using commercial off-the-shelf devices. To extract the time of flight (ToF) reflected by the moving person, the accumulated channel impulse responses (CIRs) and the corresponding variances are used to train the convolutional neural networks (CNNs) model. Particle filter algorithm is adopted to track the moving person based on the extracted ToFs of all pairs of links. Experimental results show that the proposed CIR- and variance-based CNN models achieve less than 30 cm root mean square errors. Especially, the variance-based CNN model is robust to the scenario changing and promising for practical applications.

Due to its high delay resolution, the ultra-wideband (UWB) technique has been widely adopted for fine-grained indoor localization. Instead of active positioning, UWB radar-based passive human tracking is explored using commercial off-the-shelf (COTS) devices. To extract the time-of-flight (ToF) reflected by the moving person, the accumulated channel impulse responses (CIR) and the corresponding variances are used to train the convolutional neural networks (CNN) model. Particle filter algorithm is adopted to track the moving person based on the extracted ToFs of all pairs of links. Experimental results show that the proposed CIR- and variance-based CNN models achieve less than 30-cm root-mean-square errors (RMSEs). Especially, the variance-based CNN model is robust to the scenario changing and promising for practical applications.